The Impact of Different Intermittent Irrigation Management and Planting Distances on Yield and Yield Components of Rice Plant in Northern Iran

In this study, a split plot experiment was conducted in a randomized complete blocks design with three iterations, for two years, in Gilan province (Iran), to investigate the impact of periodic irrigation and different planting distances on yield and yield components of rice plant. The irrigation was performed at five levels, I1, daily flooding irrigation (control treatment) and I2, I3, I4 and I5, every 5, 8, 10, and 15-days, respectively, as the main factor. Meanwhile, the planting distances were at four levels (S1: 20 × 20, S2: 25 × 25, S3: 15 × 30, and S4: 20 × 30 cm) as the sub factor. The simple effects of irrigation, as well as planting distance on all traits except harvest index, were significant at the level of 1%. In addition, the interaction of irrigation and planting distance on seed yield, plant height, number of seeds per panicle, biological yield, and water use were also significant at the level of 1%. The 20 × 20 planting distance resulted in the best conditions for the rice plant at different stress severities, thus, a 20 × 20 planting distance is appropriate in order to achieve good yield. Meanwhile, of the irrigation levels, 5-day irrigation led to the highest yield. The 5-day irrigation produced higher yield compared to flooding irrigation, and is therefore suitable for achieving higher yields as well as for water conservation, a major agricultural problem

A chemical probe based on the PreQ1 metabolite enables transcriptome-wide mapping of binding sites

The role of metabolite-responsive riboswitches in regulating gene expression in bacteria is well known and makes them useful systems for the study of RNA-small molecule interactions. Here, we study the PreQ1 riboswitch system, assessing sixteen diverse PreQ1-derived probes for their ability to selectively modify the class-I PreQ1 riboswitch aptamer covalently. For the most active probe (11), a diazirine-based photocrosslinking analog of PreQ1, X-ray crystallography and gel-based competition assays demonstrated the mode of binding of the ligand to the aptamer, and functional assays demonstrated that the probe retains activity against the full riboswitch. Transcriptome-wide mapping using Chem-CLIP revealed a highly selective interaction between the bacterial aptamer and the probe. In addition, a small number of RNA targets in endogenous human transcripts were found to bind specifically to 11, providing evidence for candidate PreQ1 aptamers in human RNA. This work demonstrates a stark influence of linker chemistry and structure on the ability of molecules to crosslink RNA, reveals that the PreQ1 aptamer/ligand pair are broadly useful for chemical biology applications, and provides insights into how PreQ1, which is similar in structure to guanine, interacts with human RNAs

The Alignment of Australia’s National Construction Code and the Sendai Framework for Disaster Risk Reduction in Achieving Resilient Buildings and Communities

The risks associated with extreme weather events induced by climate change are increasingly being recognized, and must be addressed through each country’s construction regulations, building codes, and standards. Ensuring that buildings and cities are resilient against disasters is becoming more important. Few studies have analyzed the impact of global polices and frameworks in reducing disaster risks and increasing resilience in built environments. This research reviews disasters associated with climate change in the Sendai Framework for Disaster Risk Reduction 2015–2030, analyzing how Australia’s National Construction Code is aligned with the framework and the potential implications for reducing disaster risk. Decision-makers in construction companies in Sydney, Australia, were surveyed. The results show there is a statistically significant link among the National Construction Code, the Sendai Framework, and building resilience. The Sendai Framework is an effective mediator in this three-pronged relationship that can further enhance building resilience in Australia. Stakeholders in the construction industry will need to incorporate disaster risk reduction practices, especially authorities, such as local governments, building commissioners, and building certifiers that are responsible for the approval, quality, and defects mitigation of development applications and best practices. Overall, implementation of the Sendai Framework will help develop more regulations and standards for resilient buildings, set targets, and make improvements over time in the Australian construction industry

A Synthesis of Express Analytic Hierarchy Process (EAHP) and Partial Least Squares-Structural Equations Modeling (PLS-SEM) for Sustainable Construction and Demolition Waste Management Assessment: The Case of Malaysia

Construction and demolition waste (CDW), as the main consequence of construction and demolition (C&D) activities, has severely affected our sustainability needs. However, construction and demolition waste management (CDWM) lacks the integration of sustainability concepts. Thus, there is a great need to include sustainability dimensions in CDWM to reach sustainable construction and demolition waste management (SCDWM). This study aims at empirically investigating SCDWM by analyzing the impacts of factors that contribute to sustainability aspects of CDWM on waste management hierarchy (WMH), including reduce, reuse, recycle, and disposal strategies. According to the literature, 26 factors were initially identified and grouped under four categories, namely environmental, economic, social, and administrative, that contribute to sustainability aspects of CDWM. Subsequently, a pilot test was performed to assess the significance and applicability of these factors in the Malaysian construction industry by implementing the express analytic hierarchy process (EAHP). Then, a questionnaire survey was performed to collect data from 132 construction companies involved in CDWM. Partial least squares-structural equation modeling (PLS-SEM) was used to test the hypothetical relationships by applying SmartPLS software. Results demonstrated that the economic aspect of CDWM (main category) and “public environment contamination due to illegal waste dumping” (sub-category) were the most influential factor in SCDWM in Malaysia

A Synthesis of Express Analytic Hierarchy Process (EAHP) and Partial Least Squares-Structural Equations Modeling (PLS-SEM) for Sustainable Construction and Demolition Waste Management Assessment: The Case of Malaysia

Construction and demolition waste (CDW), as the main consequence of construction and demolition (C&D) activities, has severely affected our sustainability needs. However, construction and demolition waste management (CDWM) lacks the integration of sustainability concepts. Thus, there is a great need to include sustainability dimensions in CDWM to reach sustainable construction and demolition waste management (SCDWM). This study aims at empirically investigating SCDWM by analyzing the impacts of factors that contribute to sustainability aspects of CDWM on waste management hierarchy (WMH), including reduce, reuse, recycle, and disposal strategies. According to the literature, 26 factors were initially identified and grouped under four categories, namely environmental, economic, social, and administrative, that contribute to sustainability aspects of CDWM. Subsequently, a pilot test was performed to assess the significance and applicability of these factors in the Malaysian construction industry by implementing the express analytic hierarchy process (EAHP). Then, a questionnaire survey was performed to collect data from 132 construction companies involved in CDWM. Partial least squares-structural equation modeling (PLS-SEM) was used to test the hypothetical relationships by applying SmartPLS software. Results demonstrated that the economic aspect of CDWM (main category) and “public environment contamination due to illegal waste dumping” (sub-category) were the most influential factor in SCDWM in Malaysia

A novel machine learning approach combined with optimization models for eco-efficiency evaluation

Machine learning approaches have been developed rapidly and also they have been involved in many academic findings and discoveries. Additionally, they are widely assessed in numerous industries such as cement companies. Cement companies in developing countries, despite many profits such as valuable mines, face many challenges. Optimization, as a key part of machine learning, has attracted more attention. The main purpose of this paper is to combine a novel Data Envelopment Analysis (DEA) approach in optimization at the first step to find the Decision-Making Unit (DMU) with innovative clustering algorithms in machine learning at the second step introduce the model and algorithm with higher accuracy. At the optimization section with converting two-stage to a simple standard single-stage model, 24 cement companies from five developing countries over 2014-2019 are compared. Window-DEA analysis is used since it leads to increase judgment on the consequences, mainly when applied to small samples followed by allowing year-by-year comparisons of the results. Applying window analysis can be beneficial for managers to expand their comparison and evaluation. To find the most accurate model CCR (Charnes, Cooper and Rhodes model), BBC (Banker, Charnes and Cooper model) and Free Disposal Hull (FDH) DEA model for measuring the efficiency of decision processes are used. FDH model allows the free disposability to construct the production possibility set. At the machine learning section, a novel three-layers data mining filtering pre-processes proposed by expert judgment for clustering algorithms to increase the accuracy and to eliminate unrelated attributes and data. Finally, the most efficient company, best performance model and the most accurate algorithm are introduced. The results indicate that the 22nd company has the highest efficiency score with an efficiency score of 1 for all years. FDH model has the highest efficiency scores during all periods compared with other suggested models. K-means algorithm receives the highest accuracy in all three suggested filtering layers. The BCC and CCR models have the second and third places, respectively. The hierarchical clustering and density-based clustering algorithms have the second and third places, correspondingly

Competitive Microarray Screening Reveals Functional Ligands for the DHX15 RNA G‑Quadruplex

RNAs are increasingly considered valuable therapeutic targets, and the development of methods to identify and validate both RNA targets and ligands is more important than ever. Here, we utilized a bioinformatic approach to identify a hairpin-containing RNA G-quadruplex (rG4) in the 5′ untranslated region (5′ UTR) of DHX15 mRNA. By using a novel competitive small molecule microarray (SMM) approach, we identified a compound that specifically binds to the DHX15 rG4 (KD = 12.6 ± 1.0 μM). This rG4 directly impacts translation of a DHX15 reporter mRNA in vitro, and binding of our compound (F1) to the structure inhibits translation up to 57% (IC50 = 22.9 ± 3.8 μM). This methodology allowed us to identify and target the mRNA of a cancer-relevant helicase with no known inhibitors. Our target identification method and the novelty of our screening approach make our work informative for future development of novel small molecule cancer therapeutics for RNA targets

Competitive Microarray Screening Reveals Functional Ligands for the DHX15 RNA G‑Quadruplex

RNAs are increasingly considered valuable therapeutic targets, and the development of methods to identify and validate both RNA targets and ligands is more important than ever. Here, we utilized a bioinformatic approach to identify a hairpin-containing RNA G-quadruplex (rG4) in the 5′ untranslated region (5′ UTR) of DHX15 mRNA. By using a novel competitive small molecule microarray (SMM) approach, we identified a compound that specifically binds to the DHX15 rG4 (KD = 12.6 ± 1.0 μM). This rG4 directly impacts translation of a DHX15 reporter mRNA in vitro, and binding of our compound (F1) to the structure inhibits translation up to 57% (IC50 = 22.9 ± 3.8 μM). This methodology allowed us to identify and target the mRNA of a cancer-relevant helicase with no known inhibitors. Our target identification method and the novelty of our screening approach make our work informative for future development of novel small molecule cancer therapeutics for RNA targets

Competitive Microarray Screening Reveals Functional Ligands for the DHX15 RNA G‑Quadruplex

RNAs are increasingly considered valuable therapeutic targets, and the development of methods to identify and validate both RNA targets and ligands is more important than ever. Here, we utilized a bioinformatic approach to identify a hairpin-containing RNA G-quadruplex (rG4) in the 5′ untranslated region (5′ UTR) of DHX15 mRNA. By using a novel competitive small molecule microarray (SMM) approach, we identified a compound that specifically binds to the DHX15 rG4 (KD = 12.6 ± 1.0 μM). This rG4 directly impacts translation of a DHX15 reporter mRNA in vitro, and binding of our compound (F1) to the structure inhibits translation up to 57% (IC50 = 22.9 ± 3.8 μM). This methodology allowed us to identify and target the mRNA of a cancer-relevant helicase with no known inhibitors. Our target identification method and the novelty of our screening approach make our work informative for future development of novel small molecule cancer therapeutics for RNA targets

Thoracic computerized tomographic (CT) findings in 2009 influenza A (H1N1) virus infection in Isfahan, Iran

Background: Pandemic 2009 H1N1 influenza A virus arrived at Isfahan in August 2009. The virus is still circulating in the world. The abnormal thoracic computerized tomographic (CT) scan findings vary widely among the studies of 2009 H1N1 influenza. We evaluated the thoracic CT findings in patients with 2009 H1N1 virus infection to describe findings compared to previously reported findings, and to suggest patterns that may be suggestive for 2009 influenza A (H1N1) in an appropriate clinical setting. Methods: Retrospectively, the archive of all patients with a diagnosis of 2009 H1N1 influenza A were reviewed, in Al-Zahra Hospital in Isfahan, central Iran, between September 23 rd 2009 to February 20 th 2010. Out of 216 patients with confirmed 2009 influenza A (H1N1) virus, 26 cases with abnormal CT were enrolled in the study. Radiologic findings were characterized by the type and pattern of opacities and zonal distribution. Results: Patchy infiltration (34.6%), lobar consolidation (30.8%), and interstitial infiltration (26.9%) with airbronchogram (38.5%) were the predominant findings in our patients. Bilateral distribution was seen in 80.8% of the patients. Only one patient (3.8%) showed ground-glass opacity, predominant radiographic finding in the previous reports and severe acute respiratory syndrome (SARS). Conclusions: The most common thoracic CT findings in pandemic H1N1 were patchy infiltration, lobar consolidation, and interstitial infiltration with airbronchogram and bilateral distribution. While these findings can be associated with other infections; they may be suggestive to 2009 influenza A (H1N1) in the appropriate clinical setting. Various radiographic patterns can be seen in thoracic CT scans of the influenza patients. Imaging findings are nonspecific